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In this article, a new strategy is proposed for soft urban land-cover extraction based on mixed training data and Support Vector Machines (SVMs). The strategy is applied to soft classification of urban water cover using Landsat 8 multispectral data in Beijing. The results are validated with extensive manual mapping data and are compared with the results derived from a well-trained SVM and the linear spectral unmixing method using only pure samples. Our experimental results indicate that the proposed strategy works effectively in extracting the water cover in the urban image. The results are better than those obtained by SVM and linear spectral unmixing with only pure samples. Our findings demonstrate that the combination of image-based mixed training data and SVM enhances the strengths of kernel-based approaches for soft mapping urban land cover from imaging data. Therefore, the proposed workflow constitutes a new flexible and extendable approach to soft mapping urban land cover. Future work includes the construction of a comprehensive image-based spectral library for urban areas, and the testing of the proposed strategy on additional land-cover types in urban areas such as grass, tree, and bare soil. [ABSTRACT FROM PUBLISHER]

In order to improve the utilization rate of spectroscopic data and texture information, this study proposes a method for optimal selection of spectrum and texture features based on automatic subspace division and rough set theory. This method takes advantage of rough set reduct ideology in order to realize the reduction of different types of ground object spectral features on the basis of the conventional subspace division method. In using this method, the primary spectral band based on spectral information can be determined. Then, the grey-level co-occurrence matrix method can be used to calculate the texture information of the primary spectral band and determine the reduction and optimization in order to obtain the final band based on the spectrum and texture information. Verification of this method is made by using CASI data of Heihe Region, China, and AVIRIS data of the Indiana Region, USA, and also using Support Vector Machine (SVM) classification of the original spectral, primary spectral, and final bands. The results indicate the following. (1) The method for optimal selection of the critical spectral band and texture band, based on the rough set theory, can efficiently improve the classification accuracy of high-spatial resolution remote-sensing images. However, the effects for the low-spatial resolution images are minimal. (2) For high-spatial-resolution remote-sensing images, such as roads, trenches, buildings, and other types of object with obvious textural features, the addition of image texture information can increase the degree of distinction of these different types and thereby improve the classification accuracy. However, the addition of the textural information for some objects with similar texture features will cause misclassification and reduce the classification accuracy for these types of images. (3) This method can realize the optimal selection of spectrum and texture bands of a hyperspectral image and has a certain universality. Also, the texture information will be richer and this method will be more practical through increasing the spatial resolution of images. [ABSTRACT FROM AUTHOR]

Combining spectral and morphological information to classify hyperspectral data offers a considerable advantage over methods based solely on spectral or morphological data. Previously, a classification method was proposed that concatenated extended morphological profile (EMP) and spectral information into one feature vector for each pixel of an image. Although this method has the merit of simultaneously using spectral and morphological information, it runs the risk of generating new spectral constituents (not present in the original image). In this letter, an improved classification method based on fusing extended multivariate morphological profile (EMMP) and spectral information was proposed. A new vector ordering method based on spectral purity-based criterion was adopted to overcome the problem of generating new constituents in EMP. A feature selection algorithm was employed to improve the efficiency of EMMP. Experiments were carried out on a hyperspectral data set collected by NASA's Airborne Visible-Infrared Imaging Spectrometer sensor. Experimental results showed that EMMP was effective at describing morphological information in hyperspectral data, and that this letter's method was superior to the previous method in terms of classification accuracy but inferior to the previous method in terms of time consumption. [ABSTRACT FROM AUTHOR]